· Superliga
Estudiantes Rio Cuarto
ATT
2/10
DEF
4/10
1:2
Gimnasia Y Esgrima La Plata
7/10
ATT
5/10
DEF
Full-time
Best Pick
Under 2.5
71%
Correct Score47′
Top
0-1
18.5%
6.00
Top
0-0
15.5%
7.50
1-1
12.4%
6.00
0-2
11.1%
8.00
1-0
10.4%
10.00
1-2
7.4%
9.00
0-3
4.4%
17.00
2-1
4.1%
15.00
121%
4.84
X31%
3.27
249%
2.05TOP
1X52%
X280%
1270%
xG — Expected Goals
Estudiantes Rio Cuarto
Estudiantes Rio Cuarto exp
Gimnasia Y Esgrima La Plata
Gimnasia Y Esgrima La Plata exp
goal
per 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Estudiantes Rio Cuarto
Gimnasia Y Esgrima La Plata
Confirmed XI5-3-2
38
Herrera
14
Gonzalez
6
Antonini
30
Ojeda
29
Quiroga
23
Rodríguez
10
González
5
Cabrera
17
Alanís
77
Hesar
11
Garnerone
Impact Player Hot Head
Estudiantes Rio Cuarto40 players
50.1/ 99Average
Top Rated
1Siro Rosané MID78
2Nicolás TalponeMID72
3Gonzalo MaffiniDEF70
Gimnasia Y Esgrima La Plata38 players
52.9/ 99Average
Top Rated
1Juan CortazzoDEF71
2Manuel PanaroFWD70
3Maximiliano Zalazar FWD70
Player Rankings
Snipers
Top goal threats
1
Maximiliano Zalazar Gimnasia Y Esgrima La Plata
99
2
Franco TorresGimnasia Y Esgrima La Plata
94
3
Manuel PanaroGimnasia Y Esgrima La Plata
84
4
Facundo GallardoEstudiantes Rio Cuarto
81
5
Joaquín RiveroEstudiantes Rio Cuarto
63
Wall
Top defenders
1
Juan CortazzoGimnasia Y Esgrima La Plata
99
2
Germán ContiGimnasia Y Esgrima La Plata
98
3
Renzo Giampaoli Gimnasia Y Esgrima La Plata
93
4
Gonzalo MaffiniEstudiantes Rio Cuarto
78
5
Enzo MartínezGimnasia Y Esgrima La Plata
72
Control
Top playmakers
1
Alejandro CabreraEstudiantes Rio Cuarto
89
2
Lucas Castro Gimnasia Y Esgrima La Plata
75
3
Ignacio MiramónGimnasia Y Esgrima La Plata
72
4
Nacho FernándezGimnasia Y Esgrima La Plata
68
5
Francesco Lo CelsoEstudiantes Rio Cuarto
64
Bad Boys
Most likely to get booked
1
Javier FerreiraEstudiantes Rio Cuarto
1
2
Ramón ÁbilaEstudiantes Rio Cuarto
1
3
Franco TorresGimnasia Y Esgrima La Plata
1
4
Maximiliano Zalazar Gimnasia Y Esgrima La Plata
1
5
Nicolas Barros SchelottoGimnasia Y Esgrima La Plata
7
Power Rankings
Strongest overall
1
Siro Rosané Estudiantes Rio Cuarto
78
2
Nicolás TalponeEstudiantes Rio Cuarto
72
3
Juan CortazzoGimnasia Y Esgrima La Plata
71
4
Gonzalo MaffiniEstudiantes Rio Cuarto
70
5
Facundo GallardoEstudiantes Rio Cuarto
70

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Estudiantes Rio Cuarto vs Gimnasia Y Esgrima La Plata based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.

The Bet Builder combines multiple selections from the Estudiantes Rio Cuarto vs Gimnasia Y Esgrima La Plata match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.

xG (expected goals) measures the quality of scoring chances. If Estudiantes Rio Cuarto has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).

Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for Estudiantes Rio Cuarto vs Gimnasia Y Esgrima La Plata without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.

A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives Estudiantes Rio Cuarto a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.

AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.

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